Wayne (Wen-Yan) Wu

I am an incoming Research Scientist at University of California, Los Angeles, working with Bolei Zhou. Since 2019, I have been a Cooperative Researcher at MMLab at Nanyang Technological University, Singapore, working with Chen Change Loy. Previously, it was pleasant to work with Dahua Lin and Xiaogang Wang closely. In June 2022, I obtained my PhD in the Department of Computer Science and Technology at Tsinghua University.
The central goal of my research is to make Artificial Intelligence (AI) systems more and more usable. An AI system includes two critical functions: 1) perceiving the real world that AI interacts with and 2) expressing the personal characteristic/action and situated environment of AI itself. To ultimately achieve the usability of AI systems, my research focuses on the following two key directions: 1) learning to perceive the real world to make the perceiving more effective with less supervision and 2) learning to generate/re-create the digital world to make the expressing easier with less human intervention. Further, my research also involves the construction of datasets and open-source software to facilitate the development of academia, centered around these two research directions. Moving forward, I am recently passionate about human-like AI systems., which will have better common sense in their perceiving system, as well as better creativity in their expressing system.
News
Jun, 2023 | OmniObject3D is selected as Best Paper Candidate at CVPR 2023. |
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Oct, 2022 | We started OpenXDLab – a new large-scale 3D dataset open-source platform! 🔥 |
Sep, 2022 | We released XRNeRF – OpenXRLab’s Neural Radiance Fields (NeRF) Toolbox! 🔥 |
Aug, 2020 | We are organizing DeeperForensics Challenge on Real-World Face Forgery Detection, ECCV 2020. |
Aug, 2020 | We are organizing Workshop on Sensing, Understanding and Synthesizing Humans, ECCV 2020. |
Jul, 2020 | We released MMAction2, OpenMMLab’s Next Generation Action Understanding Toolbox. |
Jul, 2020 | We released MMEditing, OpenMMLab’s Image and Video Editing Toolbox. |
Oct, 2019 | We are organizing Workshop on Statistical Deep Learning for Computer Vision, ICCV 2019. |
Selected Publications
- RenderMe-360: A Large Digital Asset Library and Benchmarks Towards High-fidelity Head AvatarsNeural Information Processing Systems (NeurIPS), Datasets and Benchmarks, 2023
- DNA-Rendering: A Diverse Neural Actor Repository for High-Fidelity Human-centric RenderingInternational Conference on Computer Vision (ICCV), 2023
- SynBody: Synthetic Dataset with Layered Human Models for 3D Human Perception and ModelingInternational Conference on Computer Vision (ICCV), 2023
- 3DHumanGAN: Towards Photo-Realistic 3D-Aware Human Image GenerationInternational Conference on Computer Vision (ICCV), 2023
- OmniObject3D: Large-Vocabulary 3D Object Dataset for Realistic Perception, Reconstruction and GenerationConference on Computer Vision and Pattern Recognition (CVPR), 2023
- StyleGAN-Human: A Data-Centric Odyssey of Human GenerationEuropean Conference on Computer Vision (ECCV), 2022
- CelebV-HQ: A Large-Scale Video Facial Attributes DatasetEuropean Conference on Computer Vision (ECCV), 2022
- Everything’s Talkin’: Pareidolia Face ReenactmentConference on Computer Vision and Pattern Recognition (CVPR), 2021
- TransMoMo: Invariance-Driven Unsupervised Video Motion RetargetingConference on Computer Vision and Pattern Recognition (CVPR), 2020
- TransGaGa: Geometry-Aware Unsupervised Image-to-Image TranslationConference on Computer Vision and Pattern Recognition (CVPR), 2019
- ReenactGAN: Learning to Reenact Faces via Boundary TransferEuropean Conference on Computer Vision (ECCV), 2018
- Look at Boundary: A Boundary-Aware Face Alignment AlgorithmConference on Computer Vision and Pattern Recognition (CVPR), 2018